Google Perfects 'Blade Runner-style' Photo Details

Google is working hard on Pixel Recursive Super Resolution, a method for synthesizes realistic details into images while enhancing their resolution. The problem of super resolution entails artificially enlarging
a low resolution photograph to recover a plausible
high resolution version of it

(Low res input, predicted set, ground truth set)

A low resolution image may correspond
to multiple plausible high resolution images, thus modeling
the super resolution process with a pixel independent conditional
model often results in averaging different details–
hence blurry edges. By contrast, our model is able to represent
a multimodal conditional distribution by properly modeling
the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We
employ a PixelCNN architecture to define a strong prior
over natural images and jointly optimize this prior with a
deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look
more photo realistic than a strong L2 regression baseline.

Science fiction fans recall this legendary sequence from Blade Runner, in which Deckard seems to pull inexhaustible details from a photograph using Esper photo analysis.